Conversion of CO₂ to multi-carbon products using renewable electricity is a viable strategy to reduce the greenhouse gas effects and produce high-value chemicals in a sustainable manner. However, the commercial viability of this process is severely affected due to the low selectivities, or faradaic efficiencies (FEs) of the hydrocarbons and alcohols produced. Even though there has been a lot of research towards finding better catalysts for converting CO₂ to C₂₊ products selectively, copper remains the state-of-the-art catalyst for this conversion[1]. Recent work has shown that larger hydrocarbons beyond C₂ can be formed on oxide-derived Cu and Ni catalysts with non-trivial faradaic efficiencies (~25%) [2,3]. This remains a widely underexplored area and holds great prominence due to the high value of C₃₊ products, which can further improve the economics of e-CO2RR. Even though promising, the reaction mechanisms for C₃₊ product formation involve large reaction networks with several surface intermediates (>100). In addition, electrolyte effects including pH and cation are found to influence the CO₂ activation and C-C bond formation reaction steps [1]. Therefore, in this study, we develop a detailed microkinetic model to first understand the reaction mechanism towards the various C₂ and C₃ products on Cu catalysts. We also test the influence of cations on the rate determining steps to evaluate their effect on different product selectivities. 

Firstly, we identified all the possible reaction paths for the C₂₊ products using an in-house graph-theory based algorithm ‘r-net’. We then found all the unique sites on the Cu (100) and automatically placed the reaction intermediates on the identified sites using the ‘catkit’ algorithm. Once the transition state energies were evaluated, all the energetics data has been stored in a python-based ASE database. This database serves as an input for our microkinetic modelling code ‘pyMKM’. The initial results from the model showed that most abundant surface intermediate is CO* for which the coverages are high. Hence, we performed a coverage dependent analysis on the most important reaction intermediates and reaction steps that are found through the degree of rate control analysis. Additionally, we evaluated the effect of cations on these pathways through which the lower on-set potential of C₂ products as compared to C₁ products was correctly predicted. The selectivity determining C-C bond steps that lead to propanol formation have also been ascertained. The ultimate aim of the study would be to further evaluate the identified pathways on oxide-derived Cu models to understand the influence of Cu oxidation state towards selectivity of hydrocarbon products.